Questions tagged [vgg]

For questions related to the VGG neural networks, which were proposed in "Very Deep Convolutional Networks for Large-Scale Image Recognition" (2015) by Karen Simonyan and Andrew Zisserman.

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30 views

How does a VGG-based Style-Loss incorporate color information?

I've recently been reading a lot about style transfer, its applications and implications. I understand what the Gram matrix is and does. I can program it. But one thing that has been boggling me is: ...
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1answer
60 views

Why the partial derivative is $0$ when $F_{ij}^l < 0$?. Math behind style transfer

I am currently in the process of reading and understanding the process of style transfer. I came across this equation in the research paper which went like - For context, here is the paragraph - ...
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0answers
10 views

Inconsistent Classification Accuracy between Classification Network & Object Detection

I have been working on an object detection and classification problem, and I am having understanding a discrepancy in my results. I am try to detect and classify 2 classes. These objects are ...
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1answer
44 views

Extract Features at Multiple Image-Scales

I try to replicate the results of this paper. They state, that they used VGG16- and VGG19-models pretrained on imagenet and used the output of the last convolutional layer (without relu and max-...
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2answers
169 views

Does replacing 3x3 filters with 3x1 and 1x3 filters improve the performance?

Recently I have come up with a VGG16 model for my binary classification task. I have relatively simple signal images Therefore (maybe?) other deeper models like ...
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0answers
19 views

Strategy to input and get large images in VGG neural networks

I'm using a transfert-style based deep learning approach that use VGG (neural network). The latter works well with images of small size (512x512pixels), however it provides distorted results when ...
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3answers
597 views

Is a VGG-based CNN model sometimes better for image classfication than a modern architecture?

I have an image classification task to solve, but based on quite simple/good terms: There are only two classes (either good or not good) The images always show the same kind of piece (either with or ...
5
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2answers
144 views

How do I improve accuracy and know when to stop training?

I am training a modified VGG-16 to classify crowd density (empty, low, moderate, high). 2 dropout layers were added at the end on the network each one after one of the last 2 FC layers. network ...
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3answers
288 views

How are the dimensions of the feature maps produced by the convolutional layer determined in VGG-16?

I'm trying to understand how the dimensions of the feature maps produced by the convolution are determined in a ConvNet. Let's take, for instance, the VGG-16 architecture. How do I get from 224x224x3 ...
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1answer
209 views

Why does the number of feature maps increases in the VGG model?

I found the below image of how a CNN works But I don't really understand it. I think I do understand CNNs, but I find this diagram very confusing. My simplified understanding: Features are ...
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1answer
100 views

Number of units of the last layer [closed]

I am preparing a binary classifier. Initially, I used the following parameters based on the well-known cat and dog classifier example; ...
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1answer
1k views

Trying to understand VGG convolution neural networks architecture

Trying to understand the VGG architecture and I have these following questions. I understand the general understanding of increasing filter size is because we are using max pooling and so its image ...